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https://github.com/ggml-org/llama.cpp.git
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spec : parallel drafting support (#22838)
* spec : refactor * spec : drop support for incompatible vocabs * spec : update common_speculative_init() * cont : pass seq_id * cont : dedup ctx_seq_rm_type * server : sketch the ctx_dft decode loop * server : draft prompt cache and checkpoints * server : improve ctx names * server, spec : transition to unified spec context * cont : sync main and drft contexts * cont : async drft eval when possible * cont : handle non-ckpt models * cont : pass correct n_past for drafting * cont : process images throught the draft context * spec : handle draft running out of context * server : fix mtmd draft processing * server : fix URL for draft model * server : add comment * server : clean-up + dry * speculative-simple : update * spec : fix n_past type * server : fix slot ctx_drft ptr * tools : update readme * naming : improve consistency * spec : refactor for multi-sequence speculative context * cont : prepare params * cont : prepare params * spec : support parallel drafts * server : support parallel drafting * llama : reuse device buffers when possible * server, spec : clean-up * cont : clean-up * cont : minor * spec : reset `drafting` flag at the end * spec : introduce `common_speculative_process()` * spec : allow for multiple spec types (chain of speculators) * replace old type field of type common_speculative_type in the common_params_speculative struct with a vector to allow multiple types to be specified * introduce common_get_enabled_speculative_impls(const std::vector<enum common_speculative_type>) to figure out which implementations the user has enabled * introduce common_speculative_type_from_names(const std::vector<std::string> & names) to parse the already user provided spec types * all speculators run sequentially, best one wins (we verify its drafted tokens) * maximize expected accepted tokens for current round by calculating the product between the probability of accepting current token (n_acc_tokens / n_gen_drafts) and the draft's length --------- Co-authored-by: Petros Sideris <petros.sideris@nokia.com>
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@@ -1422,7 +1422,7 @@ common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) {
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// try to remove the last tokens
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if (!llama_memory_seq_rm(mem, 0, 1, -1)) {
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LOG_WRN("%s: the target context does not support partial sequence removal\n", __func__);
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LOG_WRN("%s: the context does not support partial sequence removal\n", __func__);
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res = COMMON_CONTEXT_SEQ_RM_TYPE_FULL;
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goto done;
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}
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@@ -1960,3 +1960,102 @@ bool common_prompt_batch_decode(
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return true;
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}
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size_t common_prompt_checkpoint::size() const {
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return data_tgt.size() + data_dft.size();
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}
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bool common_prompt_checkpoint::empty() const {
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return data_tgt.empty();
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}
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void common_prompt_checkpoint::clear() {
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n_tokens = 0;
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pos_min = 0;
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pos_max = 0;
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data_tgt.clear();
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data_dft.clear();
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}
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void common_prompt_checkpoint::update_pos(
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int64_t n_tokens,
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llama_pos pos_min,
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llama_pos pos_max) {
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this->n_tokens = n_tokens;
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this->pos_min = pos_min;
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this->pos_max = pos_max;
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}
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void common_prompt_checkpoint::update_tgt(
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llama_context * ctx,
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llama_seq_id seq_id,
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llama_state_seq_flags flags) {
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if (ctx == nullptr) {
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return;
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}
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const size_t ckpt_size = llama_state_seq_get_size_ext(ctx, seq_id, flags);
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data_tgt.resize(ckpt_size);
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const size_t n = llama_state_seq_get_data_ext(ctx, data_tgt.data(), ckpt_size, seq_id, flags);
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if (n != ckpt_size) {
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GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", ckpt_size, n);
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}
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}
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void common_prompt_checkpoint::update_dft(
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llama_context * ctx,
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llama_seq_id seq_id,
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llama_state_seq_flags flags) {
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if (ctx == nullptr) {
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return;
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}
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const size_t ckpt_size = llama_state_seq_get_size_ext(ctx, seq_id, flags);
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data_dft.resize(ckpt_size);
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const size_t n = llama_state_seq_get_data_ext(ctx, data_dft.data(), ckpt_size, seq_id, flags);
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if (n != ckpt_size) {
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GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", ckpt_size, n);
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}
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}
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void common_prompt_checkpoint::load_tgt(
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llama_context * ctx,
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llama_seq_id seq_id,
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llama_state_seq_flags flags) const {
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if (ctx == nullptr) {
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return;
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}
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if (data_tgt.empty()) {
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return;
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}
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const size_t n = llama_state_seq_set_data_ext(ctx, data_tgt.data(), data_tgt.size(), seq_id, flags);
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if (n != data_tgt.size()) {
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GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", data_tgt.size(), n);
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}
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}
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void common_prompt_checkpoint::load_dft(
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llama_context * ctx,
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llama_seq_id seq_id,
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llama_state_seq_flags flags) const {
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if (ctx == nullptr) {
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return;
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}
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if (data_dft.empty()) {
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return;
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}
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const size_t n = llama_state_seq_set_data_ext(ctx, data_dft.data(), data_dft.size(), seq_id, flags);
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if (n != data_dft.size()) {
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GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", data_dft.size(), n);
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}
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}
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